U.S. patent application number 17/688697 was filed with the patent office on 2022-06-16 for real-time downhole drilling mud viscosity and density estimations.
The applicant listed for this patent is Halliburton Energy Services, Inc.. Invention is credited to Jason D. Dykstra, Xingyong Song.
Application Number | 20220186572 17/688697 |
Document ID | / |
Family ID | |
Filed Date | 2022-06-16 |
United States Patent
Application |
20220186572 |
Kind Code |
A1 |
Song; Xingyong ; et
al. |
June 16, 2022 |
REAL-TIME DOWNHOLE DRILLING MUD VISCOSITY AND DENSITY
ESTIMATIONS
Abstract
In some embodiments, a method includes operating a mud
circulation system having drilling mud flowing therethrough and
performing a plurality of measurements from a plurality of sensors
coupled to the mud circulation system. The method includes
modeling, in real-time, drilling mud flow dynamics using a
mathematical dynamics model and predicting physical states of the
drilling mud with the mathematical dynamics model. Further, the
method described herein includes inputting the measurements into
the mathematical dynamics model and adapting the mathematical
dynamics model based, at least in part, on discrepancies between
the model physical state predictions and the measurements. The
method further includes changing an operational parameter of the
mud circulation system based on at least one value derived from the
adapted mathematics dynamics model.
Inventors: |
Song; Xingyong; (Houston,
TX) ; Dykstra; Jason D.; (Spring, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Halliburton Energy Services, Inc. |
Houston |
TX |
US |
|
|
Appl. No.: |
17/688697 |
Filed: |
March 7, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15323836 |
Jan 4, 2017 |
11268334 |
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PCT/US2016/042020 |
Jul 13, 2016 |
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17688697 |
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62191727 |
Jul 13, 2015 |
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International
Class: |
E21B 21/00 20060101
E21B021/00; E21B 21/08 20060101 E21B021/08; E21B 47/10 20060101
E21B047/10; G06F 30/20 20060101 G06F030/20; E21B 41/00 20060101
E21B041/00; G06F 17/11 20060101 G06F017/11; G06N 5/04 20060101
G06N005/04 |
Claims
1. A method of operating a system comprising a mud circulation
system having drilling mud flowing therethrough, the method
comprising: performing a plurality of measurements from a plurality
of sensors coupled to the mud circulation system; modeling in
real-time drilling mud flow dynamics in the drilling mud using a
mathematical dynamics model; predicting physical states of the
drilling mud with the mathematical dynamics model, thereby
producing model physical state predictions; inputting the
measurements into the mathematical dynamics model; and adapting the
mathematical dynamics model based at least in part on discrepancies
between the model physical state predictions and the measurements;
and changing an operational parameter of the mud circulation system
based on at least one value derived from the adapted mathematics
dynamics model.
2. The method of claim 1 further comprising: estimating an
uncertainty interval of the plurality of measurements and the
mathematical dynamics model; and updating the uncertainty intervals
for the mathematical dynamics model and the measurements, thereby
producing an updated mathematical dynamics model.
3. The method of claim 2 further comprising: repeating foregoing
steps: performing, modeling, estimating, and inputting steps with
the updated mathematical dynamics model.
4. The method of claim 2, further comprising: calculating a
real-time downhole density of the drilling mud using the updated
mathematical dynamics model.
5. The method of claim 2, further comprising: calculating a
real-time downhole viscosity of the drilling mud using the updated
mathematical dynamics model.
6. The method of claim 2, further comprising: calculating a
real-time downhole density, a real-time downhole viscosity, or both
of the drilling mud using the updated mathematical dynamics model;
and calculating an equivalent circulating density based on the
real-time downhole density, the real-time downhole viscosity, or
both.
7. The method of claim 6 further comprising: changing a composition
of the drilling mud of the mud circulation system based on the
equivalent circulating density.
8. The method of claim 1, wherein at least one of the model
physical state predictions comprises a prediction of a density of
the drilling mud or a prediction of a viscosity of the drilling
mud.
9. The method of claim 1, wherein predicting the physical states of
the drilling mud with the mathematical dynamics model, thereby
producing the model physical state predictions comprises generating
a fusion-determined drilling mud physical state value.
10. The method of claim 2, wherein updating the uncertainty
intervals for the mathematical dynamics model and the measurements,
thereby producing the updated mathematical dynamics model comprises
utilizing a feedback loop to improve an accuracy of the
mathematical dynamics model.
11. A non-transitory computer-readable medium encoded with
instructions that, when executed, cause a system comprising a mud
circulation system having drilling mud flowing therethrough to
perform a method comprising: receiving a plurality of measurements
from a plurality of sensors coupled to the mud circulation system;
modeling in real-time drilling mud flow dynamics in the drilling
mud using a mathematical dynamics model; predicting physical states
of the drilling mud with the mathematical dynamics model, thereby
producing model physical state predictions; inputting the
measurements into the mathematical dynamics model; and adapting the
mathematical dynamics model based at least in part on discrepancies
between the model physical state predictions and the measurements;
and changing an operational parameter of the mud circulation system
based on at least one value derived from the adapted mathematics
dynamics model.
12. The non-transitory computer-readable medium of claim 11,
wherein the method further comprises: estimating an uncertainty
interval of the plurality of measurements and the mathematical
dynamics model; and updating the uncertainty intervals for the
mathematical dynamics model and the measurements, thereby producing
an updated mathematical dynamics model.
13. The non-transitory computer-readable medium of claim 12,
wherein the method further comprises: calculating a real-time
downhole density of the drilling mud using the updated mathematical
dynamics model.
14. The non-transitory computer-readable medium of claim 12,
wherein the method further comprises: calculating a real-time
downhole viscosity of the drilling mud using the updated
mathematical dynamics model.
15. The non-transitory computer-readable medium of claim 11,
wherein predicting the physical states of the drilling mud with the
mathematical dynamics model, thereby producing the model physical
state predictions comprises generating a fusion-determined drilling
mud physical state value.
16. The non-transitory computer-readable medium of claim 15 further
comprising: merging, through probability-based techniques, the
plurality of measurements from the plurality of sensors to account
for redundancies in the measurements.
17. The non-transitory computer-readable medium of claim 12,
wherein updating the uncertainty intervals for the mathematical
dynamics model and the measurements, thereby producing the updated
mathematical dynamics model comprises utilizing a feedback loop to
improve an accuracy of the mathematical dynamics model.
18. The non-transitory computer-readable medium of claim 12,
wherein the method further comprises: calculating a real-time
downhole density, a real-time downhole viscosity, or both of the
drilling mud using the updated mathematical dynamics model; and
calculating an equivalent circulating density based on the
real-time downhole density, the real-time downhole viscosity, or
both.
19. The non-transitory computer-readable medium of claim 18 further
comprising: changing a composition of the drilling mud of the mud
circulation system based on the equivalent circulating density.
20. The non-transitory computer-readable medium of claim 11,
wherein at least one of the model physical state predictions
comprises a prediction of a density of the drilling mud or a
prediction of a viscosity of the drilling mud.
Description
BACKGROUND
[0001] The accurate and reliable knowledge of drilling mud
properties, especially the density and viscosity, at the drill bit
is valuable. However, due to the lack of sensors close to the drill
bit, it can be challenging to obtain the appropriate information in
a timely manner. Because the fluid properties can typically only be
measured or tested on the surface, there is a delay between the
measurement and the current fluid property down-hole. In other
words, most "real-time" measurements are on the fluids that have
circulated back to the surface, which may have different properties
from the fluids closer to the drill bit.
BRIEF DESCRIPTION OF THE DRAWINGS
[0002] The following figures are included to illustrate certain
aspects of the embodiments, and should not be viewed as exclusive
embodiments. The subject matter disclosed is amenable to
considerable modifications, alterations, combinations, and
equivalents in form and function, as will occur to those skilled in
the art and having the benefit of this disclosure.
[0003] FIG. 1 illustrates an exemplary mud circulation system
suitable for implementing the methods described herein.
[0004] It should be understood, however, that the specific
embodiments given in the drawings and detailed description thereto
do not limit the disclosure. On the contrary, they provide the
foundation for one of ordinary skill to discern the alternative
forms, equivalents, and modifications that are encompassed together
with one or more of the given embodiments in the scope of the
appended claims.
DETAILED DESCRIPTION
[0005] Disclosed herein are methods and systems for enhancing
workflow performance in the oil and gas industry. More
specifically, the present application relates to estimating the
properties of drilling muds located downhole with methods that
utilize real-time data, estimated drilling mud properties, and
mathematical models. Further, the methods described herein account
for the uncertainties induced by sensor readings and dynamic
modeling so they may be managed and/or treated systematically in
the estimation framework to enable optimal estimation with low
uncertainties.
[0006] FIG. 1 illustrates an exemplary mud circulation system 100
(e.g., a drilling system) suitable for implementing the methods
described herein. While FIG. 1 generally depicts a land-based
drilling assembly, those skilled in the art will readily recognize
that the principles described herein are equally applicable to
subsea drilling operations that employ floating or sea-based
platforms and rigs, without departing from the scope of the
disclosure.
[0007] As illustrated, the drilling assembly 100 may include a
drilling platform 102 that supports a derrick 104 having a
traveling block 106 for raising and lowering a drill string 108.
The drill string 108 may include, but is not limited to, drill pipe
and coiled tubing, as generally known to those skilled in the art.
A kelly 110 supports the drill string 108 as it is lowered through
a rotary table 112. A drill bit 114 is attached to the distal end
of the drill string 108 and is driven either by a downhole motor
and/or via rotation of the drill string 108 from the well surface.
As the bit 114 rotates, it creates a borehole 116 that penetrates
various subterranean formations 118.
[0008] A pump 120 (e.g., a mud pump) circulates mud 122 through a
feed pipe 124 and to the kelly 110, which conveys the mud 122
downhole through the interior of the drill string 108 and out
through one or more orifices in the drill bit 114. The mud 122 is
then circulated back to the surface via an annulus 126 defined
between the drill string 108 and the walls of the borehole 116. At
the surface, the recirculated or spent mud 122 exits the annulus
126 and may be conveyed through chokes 136 (also referred to as a
choke manifold) to one or more mud cleaning unit(s) 128 (e.g., a
shaker, a centrifuge, a hydrocyclone, a separator (including
magnetic and electrical separators), a desilter, a desander, a
separator, a filter, a heat exchanger, any fluid reclamation
equipment, and the like) via an interconnecting flow line 130.
After passing through the mud cleaning unit(s) 128, a "cleaned" mud
122 is deposited into a nearby retention pit 132 (e.g., a mud pit
or mud tank). While illustrated at the outlet of the wellbore 116
via the annulus 126, those skilled in the art will readily
appreciate that the mud cleaning unit(s) 128 may be arranged at any
other location in the drilling assembly 100 to facilitate its
proper function without departing from the scope of the scope of
the disclosure.
[0009] At the retention pit 132 (or before or after), the mud
circulation system may include one or more mud treatment units. The
mud 122 may be treated to change its composition and properties.
For example, weighting agents like barite may be added to the mud
122 to increase its density. In another example, base fluid may be
added to the mud 122 to decrease its density. In the illustrated
mud circulation system 100, the addition of materials to the mud
122 may be achieved with a mixer 134 communicably coupled to or
otherwise in fluid communication with the retention pit 132. The
mixer 134 may include, but is not limited to, mixers, mixing
hopper, flow paths, and related mixing equipment known to those
skilled in the art. In other embodiments, however, the materials
may be added to the mud 122 at any location in the drilling
assembly 100. In at least one embodiment, for example, there could
be more than one retention pit 132, such as multiple retention pits
132 in series. Moreover, the retention pit 132 may be
representative of one or more fluid storage facilities and/or units
where the materials may be stored, reconditioned, and/or regulated
until added to the mud 122.
[0010] The various components of the mud circulation system 100 may
further include one or more sensors, gauges, pumps, compressors,
and the like used store, monitor, regulate, convey, and/or
recondition the exemplary muds 122 (e.g., sensors and gauges to
measure the composition and/or pressure of the mud, compressors to
change the pressure of the mud, and the like).
[0011] While not specifically illustrated herein, the disclosed the
disclosed mud circulation system 100 may further include drill
collars, mud motors, downhole motors and/or pumps associated with
the drill string 108, MWD/LWD tools and related telemetry
equipment, sensors 140 or distributed sensors associated with the
components of the system 100 (e.g., the drill string 108, the
retention pit 132, the choke 136, the feed pipe 124, the pump 120,
and the kelly 110, and the mud cleaning unit(s) 128), downhole heat
exchangers, valves and corresponding actuation devices, tool seals,
packers and other wellbore isolation devices or components, and the
like. The mud circulation system 100 may also further include a
control system 138 communicably coupled to various components of
the mud circulation system 100 (e.g., sensors 140) and be capable
of executing the mathematical algorithms, methods, and mud
circulation system control described herein.
[0012] As illustrated, system 100 includes six sensors: sensor 140a
along the feed pipe proximal to the pump 120 relative to the kelly
110 for measuring mud pressure, sensor 140b along the feed pipe
just before the kelly 110 for measuring mud flow rate, sensor 140c
in or at the wellbore 116 for measuring mud pressure, sensor 140d
at a mud cleaning unit 128 like a shaker for measuring mud weight,
sensor 140e at the retention pit 132 for measuring the mud
viscosity, and sensor 140f at the mud pit 132 for measuring mud
density.
[0013] Some embodiments may provide effective and reliable downhole
estimations of mud properties using an estimator with at least four
components: a mathematical dynamics model (also referred to as the
dynamics model), a measurement fusion mechanism, a feedback loop,
and an uncertainty mechanism.
[0014] As used herein, the term "mathematical dynamics model"
refers to a series of algorithms that describes the drilling mud
flow dynamics together with the dynamics for drill bit cuttings in
real time. As used herein, the term "measurement fusion mechanism"
refers to a series of probability-based algorithms that fuses all
the measurements from the sensors 140. As used herein, the term
"uncertainty mechanism" refers to a series of algorithms that
calculate and update the uncertainty intervals for the dynamics
model.
[0015] First, the mathematical dynamics model is used to model an
approximate downhole fluid density and viscosity can be
calculated.
[0016] Second, the measurement fusion mechanism fuses all the
measurements including the fluid viscosity and density measurement
performed at the surface, the flow pressure measurement at
different locations along the flow passage, the temperature
measurement, and other measurements made in the mud circulation
system. The fusion of all the available sensor measurements should
be conducted in a systematical fashion and may improve the downhole
fluid property estimates by enhancing the robustness of the dynamic
model. Moreover, if there are redundancies in sensor measurement
(for instance, multiple sensors measuring viscosity of the drilling
mud), then the readings from different sensors can be fused
together with probability-based techniques, so that the viscosity
measurement estimation after combining these reading can have
better accuracy and lower uncertainty.
[0017] Third, the fusion of these measurements may then be fed back
via the feedback loop to the dynamics model to adjust any
discrepancies between the model states prediction and the actual
measurements. This allows for the model to be adapted to offer
better estimations of real-time downhole mud properties.
[0018] The last component is a mechanism of determining/updating
the uncertainty intervals for the dynamics model estimation, so
that the corrected uncertainty level can be used for estimation in
the following iteration.
[0019] An example of the dynamics model describing the mud flow
dynamics is described in the following equations. In some
instances, the dynamics model may be in other forms based on
different assumptions on the fluid dynamics. No matter the form of
the dynamics model, the sensor fusion method may be applied in a
similar way.
[0020] Referring again to FIG. 1, from the pump 120 to the
retention pit 132, the entire fluid circulation system forms a
closed loop, which could be further divided into N sections. In the
present example, the mud circulation system is divided into 6
sections 142: section 142a from the pump 120 through a portion of
the feed pipe 124 (illustrated between point 144a and point 144b),
section 142b from the feed pipe 124 to the kelly 110 (illustrated
between point 144b and point 144c), section 142c from the kelly 110
to through a portion of the drill string 108 (illustrated between
point 144c and point 144d), section 142d from the drill string 108
through a portion of the annulus between the drill string 108 and
the wellbore 116 (illustrated between point 144d and point 144e),
section 142e from the annulus to the choke 136 (illustrated between
point 144e and point 1440, and section 142f from the choke 136 to
the retention pit 132 (illustrated between point 144f and point
144g). This illustrates an exemplary section division that
alternatively may be done in a symmetric manner where half of the
sections are in the flowing-down loop (i.e., flow from surface to
the bottom), and the other half of the sections are in the return
loop (i.e., flow with cuttings circulating back to the
surface).
[0021] Once the system 100 is divided into N number of sections
142, the rate of change of pressure ({dot over (P)}.sub.1, {dot
over (P)}.sub.2, . . . , {dot over (P)}.sub.N) are calculated for
each section 142 using the dynamics models described in Equations
(1)-(5).
.times. I .times. .times. .omega. pump . = T engine - P pump
.times. Disp pump Equation .times. .times. ( 1 ) P . 1 = .beta. v 1
.function. [ A oriface .times. .times. 1 .times. C d .times. 2
.times. ( P pump - P 1 + .rho. 0 .times. gL 0 ) .rho. 1 - A oriface
.times. .times. 2 .times. C d .times. 2 .times. ( P 1 - P 2 + .rho.
1 .times. gL 1 ) .rho. 1 ] + other .times. .times. terms Equation
.times. .times. ( 2 ) P . 2 = .beta. v 2 .function. [ A oriface
.times. .times. 2 .times. C d .times. 2 .times. ( P 1 - P 2 + .rho.
1 .times. gL 1 ) .rho. 2 - A oriface .times. .times. 3 .times. C d
.times. 2 .times. ( P 2 - P 3 + .rho. 2 .times. gL 2 ) .rho. 2 ] +
other .times. .times. terms Equation .times. .times. ( 3 ) P . N 2
+ 1 = .times. .times. .times. .beta. v N 2 + 1 [ A oriface .times.
.times. ( N 2 + 1 ) .times. C d .times. 2 .times. ( P N 2 - P N 2 +
1 + .rho. N 2 .times. gL N 2 .rho. N 2 + 1 - .times. .times. A
oriface .times. .times. ( N 2 + 2 ) .times. C d .times. 2 .times. (
P N 2 + 1 - P N 2 + 2 + .rho. N 2 + 1 .times. gL N 2 + 1 ) .rho. N
2 + 1 ] + other .times. .times. terms Equation .times. .times. ( 4
) P . N = .times. .times. .beta. v N .function. [ A oriface .times.
.times. N .times. C d .times. 2 .times. ( P N - 1 - P N + .rho. N -
1 .times. gL N - 1 ) .rho. N - .times. .times. A oriface .times.
.times. ( tank ) .times. C d .times. 2 .times. ( P N - P tank +
.rho. N .times. gL N ) .rho. N ] + other .times. .times. terms
Equation .times. .times. ( 5 ) ##EQU00001##
where P.sub.1, P.sub.2, . . . , P.sub.N are the pressures in the
chamber sections divided as shown in FIG. 1; P.sub.pump is the
pressure at the pump 120, P.sub.tank is the pressure at the
retention pit 132, c.sub.d is the flow discharge coefficient; .rho.
is the fluid/mud density for each specific chamber; g is the
standard gravity, A.sub.oriface is the area of the fluid channel
cross-section, v is the chamber volume for each section divided,
.beta. is the fluid bulk modulus, .omega..sub.pump is the pump
rotational speed, T.sub.engine is the engine torque driving the mud
pump, and L is the chamber length of each divided section.
[0022] Given the dynamic model above, a series of model outputs can
be selected, where they are typically selected if sensors exist to
measure those variables. For example, the dynamics models described
in Equations (1)-(5) may be used to model the mud circulation
system 100. Then, based on that model, properties of each section
142 may be calculated. The choice of which property to output for
each section 142 may be based on the sensors in the section 142.
For example, section 142f includes viscosity sensor 140e and
density sensor 140f, so the output for section 142f may be
viscosity and density. Equations (6)-(11) are exemplary algorithms
for deriving properties from the modeled mud circulation system,
where y.sub.1 and y.sub.2 are pressures, y.sub.3 is flow rate,
y.sub.4 is mud cutting weights per minute, y.sub.5 is viscosity,
and y.sub.6 is density.
.times. Equation .times. .times. ( 6 ) ##EQU00002## y 1 = P 1
##EQU00002.2## .times. Equation .times. .times. ( 7 )
##EQU00002.3## y 2 = P i ##EQU00002.4## .times. Equation .times.
.times. ( 8 ) ##EQU00002.5## y 3 = A orifaceN .times. C d .times. 2
.times. ( P N - P N - 1 - .rho. N .times. gL N ) .rho. N
##EQU00002.6## .times. Equation .times. .times. ( 9 )
##EQU00002.7## y 4 = f .function. ( ROP , rock .times. .times.
density ) ##EQU00002.8## .times. Equation .times. .times. ( 10 )
##EQU00002.9## y 5 = y 4 y 4 + .rho. clean .times. .times. mud
.times. A oriface .times. .times. ( N 2 + 1 ) C d .times. 2 .times.
( P N 2 - P N 2 + 1 - .rho. N 2 .times. gL N 2 ) .rho. N 2 + 1
.times. viscosity cuttings + .rho. clean .times. .times. mud
.times. A oriface .times. .times. ( N 2 + 1 ) .times. C d .times. 2
.times. ( P N 2 - P N 2 + 1 - .rho. N 2 .times. gL N 2 ) .rho. N 2
+ 1 y 4 + .rho. clean .times. .times. mud .times. A oriface .times.
.times. ( N 2 + 1 ) .times. C d .times. 2 .times. ( P N 2 - P N 2 +
1 - .rho. N 2 .times. gL N 2 ) .rho. N 2 + 1 .times. viscosity
clean .times. .times. mud ##EQU00002.10## .times. Equation .times.
.times. ( 11 ) ##EQU00002.11## y 6 = .rho. clean .times. .times.
mud .times. A oriface .times. .times. ( N 2 + 1 ) .times. C d
.times. 2 .times. ( P N 2 - P N 2 + 1 - .rho. N 2 .times. gL N 2 )
.rho. N 2 + 1 A oriface .times. .times. ( N 2 + 1 ) .times. C d
.times. 2 .times. ( P N 2 - P N 2 + 1 - .rho. N 2 .times. gL N 2 )
.rho. N 2 + 1 ##EQU00002.12##
where the flow rate y.sub.3 is calculated based on the pressure
difference between two adjacent chambers, the weight of cuttings
y.sub.4 at the drill bit is a function of rate of penetration
(ROP), rock density, and the like, the viscosity y.sub.6 at the
drill bit is obtained based on the viscosity of the cuttings and
that of the clean mud together with an estimate of fractions of the
cuttings and mud at the downhole fluid, and the mud density at the
drill bit is estimated with the input of cuttings weight and clean
mud weight at the drill bit.
[0023] While the foregoing calculations are based on pressure,
drilling mud flow rate may be alternatively used in the
equations.
[0024] The dynamics model above can be discretized and put in a
generic form as:
X(k+1)=f.sub.flow
dynamics[X(k),T.sub.engine]+uncertainty.sub.model(k)
Y(k+1)=h[X(k)]+uncertainty.sub.meausre(k) Equations (12)
where X(k) is a vector containing all the state variables (P.sub.1,
P.sub.2, . . . ) at step k; f.sub.flow dynamics is the flow
dynamics function representing the flow/pressure dynamics Equations
(1)-(6); Y is the vector containing all the output variables (e.g.,
the various y.sub.1-y.sub.6 for each section 142);
uncertainty.sub.model is a vector containing the modeling
uncertainty and noise terms; and uncertainty.sub.measure is a
vector containing the measurement uncertainty and noise terms.
[0025] The sensor fusion algorithm estimating the viscosity and
density may be summarized as the following equations:
{circumflex over (X)}(k+1)=f.sub.flow dynamics[{circumflex over
(X)}(k),T.sub.engine]+L(k)(Y.sub.measured(k-N.sub.delay)-h[{circumflex
over (X)}(k-N.sub.delay)])
(k)=h[{circumflex over (X)}(k)]
viscosity.sub.estimate(k)=[0 0 0 0 1 0].times.h[{circumflex over
(X)}(k)]
density.sub.estimate(k)=[0 0 0 0 0 1].times.h[{circumflex over
(X)}(k)] Equations (13)
where {circumflex over (X)}(k) is the estimated state of pressures,
downhole fluid conditions, and the like;
Y.sub.measured(k-N.sub.delay) is the measured outputs (a vector
containing the fluid density measurement, viscosity measurement,
flow rate measurement, pressure measurement, cutting weight
measurement, and the like) back in N.sub.delay sampling time ago;
N.sub.delay is the total number of sampling time needed for the
cuttings to be flushed from downhole to the surface (which is
dependent on the length of the well); and L(k) is the gain
(described further below in Equations (14)).
[0026] The fusion method may fuse the physical measurement with a
dynamic model by embedding the difference between the physical
measurement and the model estimate. Since the measurement of the
downhole fluid property happens only after the fluid is circulated
back to the surface, the difference between the measurement and
output estimate a certain time before is used to adjust the
downhole fluid property estimate in real-time.
[0027] L(k) is the gain used for the sensor fusion framework. It is
determined based on the model uncertainty (uncertainty.sub.model)
together with measurement uncertainty (uncertainty.sub.measure)
as:
P(k)=J.sub.funcertainty(k-1)J.sub.f.sup.T+Q(k-1)
L(k)=P(k)J.sub.k.sup.T[J.sub.hP(k)J.sub.h+R(k)].sup.-1
uncertainty(k)=[I-L(k)J.sub.h]P(k) Equations (14)
where J.sub.f and J.sub.h are the Jacobian matrix the nonlinear
function f and h, respectively; Q is the dynamics system process
noise covariance matrix of uncertainty.sub.model; R is the
covariance matrix for the measured noise/uncertainty for
uncertainty.sub.measure; both Q and R are predetermined by off-line
calibration or empirical estimation; and uncertainty(k) is the
level of uncertainty for the estimated viscosity and density at
step k from the sensor fusion algorithm.
[0028] As illustrated in the above description, the sensor fusion
algorithm fused different sensor measurements (e.g., pressure,
density, viscosity, flow rate, and the like) with a physical
dynamic model and also a probability-based uncertainty model for
each sensor 140, so that the downhole viscosity and density
information may be estimated based on the fusion of different
sensor readings. Moreover, the sensor measurement for viscosity and
density after the fluid is circulated back to the surface may be
used to update/adapt the uncertainty interval for the real time
viscosity/density estimate using a Kalman filter (as described in
Equations (13)-(14)), Bayesian probability analysis, particle
filter, learning algorithm (e.g., neural networks), and the
like.
[0029] The downhole viscosity and density information may be used
to determine the equivalent circulating density (ECD). Further, the
parameters of the mud circulation system (e.g., weight on bit,
drilling mud flow rate, choke control, drill bit revolutions per
minute, and the like) and/or the composition of the drilling mud
(e.g., concentration of weighting agent, viscosifier, and the like)
may be adjusted to achieve a desired downhole viscosity, desired
downhole density, and/or desired ECD.
[0030] Numerous other variations and modifications will become
apparent to those skilled in the art once the above disclosure is
fully appreciated. It is intended that the following claims be
interpreted to embrace all such variations, modifications and
equivalents. In addition, the term "or" should be interpreted in an
inclusive sense.
[0031] The control system(s) described herein and corresponding
computer hardware used to implement the various illustrative
blocks, modules, elements, components, methods, and algorithms
described herein can include a processor configured to execute one
or more sequences of instructions, programming stances, or code
stored on a non-transitory, computer-readable medium. The processor
can be, for example, a general-purpose microprocessor, a
microcontroller, a digital signal processor, an application
specific integrated circuit, a field programmable gate array, a
programmable logic device, a controller, a state machine, a gated
logic, discrete hardware components, an artificial neural network,
or any like suitable entity that can perform calculations or other
manipulations of data. In some embodiments, computer hardware can
further include elements such as, for example, a memory (e.g.,
random access memory (RAM), flash memory, read only memory (ROM),
programmable read only memory (PROM), erasable programmable read
only memory (EPROM)), registers, hard disks, removable disks,
CD-ROMS, DVDs, or any other like suitable storage device or
medium.
[0032] Executable sequences described herein can be implemented
with one or more sequences of code contained in a memory. In some
embodiments, such code can be read into the memory from another
machine-readable medium. Execution of the sequences of instructions
contained in the memory can cause a processor to perform the
process steps described herein. One or more processors in a
multi-processing arrangement can also be employed to execute
instruction sequences in the memory. In addition, hard-wired
circuitry can be used in place of or in combination with software
instructions to implement various embodiments described herein.
Thus, the present embodiments are not limited to any specific
combination of hardware and/or software.
[0033] As used herein, a machine-readable medium will refer to any
medium that directly or indirectly provides instructions to a
processor for execution. A machine-readable medium can take on many
forms including, for example, non-volatile media, volatile media,
and transmission media. Non-volatile media can include, for
example, optical and magnetic disks. Volatile media can include,
for example, dynamic memory. Transmission media can include, for
example, coaxial cables, wire, fiber optics, and wires that form a
bus. Common forms of machine-readable media can include, for
example, floppy disks, flexible disks, hard disks, magnetic tapes,
other like magnetic media, CD-ROMs, DVDs, other like optical media,
punch cards, paper tapes and like physical media with patterned
holes, RAM, ROM, PROM, EPROM and flash EPROM.
Example Embodiments
[0034] Embodiment #1: A method of operating a system comprising a
mud circulation system having drilling mud flowing therethrough,
the method comprising: performing a plurality of measurements from
a plurality of sensors coupled to the mud circulation system;
modeling in real-time drilling mud flow dynamics in the drilling
mud using a mathematical dynamics model; predicting physical states
of the drilling mud with the mathematical dynamics model, thereby
producing model physical state predictions; inputting the
measurements into the mathematical dynamics model; and adapting the
mathematical dynamics model based at least in part on discrepancies
between the model physical state predictions and the measurements;
and changing an operational parameter of the mud circulation system
based on at least one value derived from the adapted mathematics
dynamics model.
[0035] Embodiment #2: The method of Embodiment 1, further
comprising: estimating an uncertainty interval of the plurality of
measurements and the mathematical dynamics model; and updating the
uncertainty intervals for the mathematical dynamics model and the
measurements, thereby producing an updated mathematical dynamics
model.
[0036] Embodiment #3: The method of Embodiment 2 further
comprising: repeating foregoing steps: performing, modeling,
estimating, and inputting steps with the updated mathematical
dynamics model.
[0037] Embodiment #4: The method of any one of Embodiments 2-3,
further comprising: calculating a real-time downhole density of the
drilling mud using the updated mathematical dynamics model.
[0038] Embodiment #5: The method of any one of Embodiments 2-4,
further comprising: calculating a real-time downhole viscosity of
the drilling mud using the updated mathematical dynamics model.
[0039] Embodiment #6: The method of any one of Embodiments 2-5,
further comprising: calculating a real-time downhole density, a
real-time downhole viscosity, or both of the drilling mud using the
updated mathematical dynamics model; and calculating an equivalent
circulating density based on the real-time downhole density, the
real-time downhole viscosity, or both.
[0040] Embodiment #7: The method of Embodiment 6, further
comprising: changing a composition of the drilling mud of the mud
circulation system based on the equivalent circulating density.
[0041] Embodiment #8: The method of any one of Embodiments 1-7,
wherein at least one of the model physical state predictions
comprises a prediction of a density of the drilling mud or a
prediction of a viscosity of the drilling mud.
[0042] Embodiment #9: The method of any one of Embodiments 1-8,
wherein predicting the physical states of the drilling mud with the
mathematical dynamics model, thereby producing the model physical
state predictions comprises generating a fusion-determined drilling
mud physical state value.
[0043] Embodiment #10: The method of any one of Embodiments 2-9,
wherein updating the uncertainty intervals for the mathematical
dynamics model and the measurements, thereby producing the updated
mathematical dynamics model comprises utilizing a feedback loop to
improve an accuracy of the mathematical dynamics model.
[0044] Embodiment #11: A non-transitory computer-readable medium
encoded with instructions that, when executed, cause a system
comprising a mud circulation system having drilling mud flowing
therethrough to perform a method comprising: receiving a plurality
of measurements from a plurality of sensors coupled to the mud
circulation system; modeling in real-time drilling mud flow
dynamics in the drilling mud using a mathematical dynamics model;
predicting physical states of the drilling mud with the
mathematical dynamics model, thereby producing model physical state
predictions; inputting the measurements into the mathematical
dynamics model; and adapting the mathematical dynamics model based
at least in part on discrepancies between the model physical state
predictions and the measurements; and changing an operational
parameter of the mud circulation system based on at least one value
derived from the adapted mathematics dynamics model.
[0045] Embodiment #12: The non-transitory computer-readable medium
of Embodiment 11, wherein the method further comprises: estimating
an uncertainty interval of the plurality of measurements and the
mathematical dynamics model; and updating the uncertainty intervals
for the mathematical dynamics model and the measurements, thereby
producing an updated mathematical dynamics model.
[0046] Embodiment #13: The non-transitory computer-readable medium
of Embodiment 12, wherein the method further comprises: calculating
a real-time downhole density of the drilling mud using the updated
mathematical dynamics model.
[0047] Embodiment #14: The non-transitory computer-readable medium
of any one of Embodiments 12-13, wherein the method further
comprises: calculating a real-time downhole viscosity of the
drilling mud using the updated mathematical dynamics model.
[0048] Embodiment #15: The non-transitory computer-readable medium
of any one of Embodiments 11-14, wherein predicting the physical
states of the drilling mud with the mathematical dynamics model,
thereby producing the model physical state predictions comprises
generating a fusion-determined drilling mud physical state
value.
[0049] Embodiment #16: The non-transitory computer-readable medium
of Embodiment 15 further comprising: merging, through
probability-based techniques, the plurality of measurements from
the plurality of sensors to account for redundancies in the
measurements.
[0050] Embodiment #17: The non-transitory computer-readable medium
of any one of Embodiments 12-16, wherein updating the uncertainty
intervals for the mathematical dynamics model and the measurements,
thereby producing the updated mathematical dynamics model comprises
utilizing a feedback loop to improve an accuracy of the
mathematical dynamics model.
[0051] Embodiment #18: The non-transitory computer-readable medium
of any one of Embodiments 12-17, wherein the method further
comprises: calculating a real-time downhole density, a real-time
downhole viscosity, or both of the drilling mud using the updated
mathematical dynamics model; and calculating an equivalent
circulating density based on the real-time downhole density, the
real-time downhole viscosity, or both.
[0052] Embodiment #19: The non-transitory computer-readable medium
of Embodiment 18 further comprising: changing a composition of the
drilling mud of the mud circulation system based on the equivalent
circulating density.
[0053] Embodiment #20: The non-transitory computer-readable medium
of any one of Embodiments 11-19, wherein at least one of the model
physical state predictions comprises a prediction of a density of
the drilling mud or a prediction of a viscosity of the drilling
mud.
[0054] Numerous other variations and modifications will become
apparent to those skilled in the art once the above disclosure is
fully appreciated. It is intended that the following claims be
interpreted to embrace all such variations, modifications and
equivalents. In addition, the term "or" should be interpreted in an
inclusive sense.
[0055] Unless otherwise indicated, all numbers expressing
quantities of ingredients, properties such as molecular weight,
reaction conditions, and so forth used in the present specification
and associated claims are to be understood as being modified in all
instances by the term "about." Accordingly, unless indicated to the
contrary, the numerical parameters set forth in the following
specification and attached claims are approximations that may vary
depending upon the desired properties sought to be obtained by the
embodiments of the present invention. At the very least, and not as
an attempt to limit the application of the doctrine of equivalents
to the scope of the claim, each numerical parameter should at least
be construed in light of the number of reported significant digits
and by applying ordinary rounding techniques.
[0056] One or more illustrative embodiments incorporating the
invention embodiments disclosed herein are presented herein. Not
all features of a physical implementation are described or shown in
this application for the sake of clarity. It is understood that in
the development of a physical embodiment incorporating the
embodiments of the present invention, numerous
implementation-specific decisions must be made to achieve the
developer's goals, such as compliance with system-related,
business-related, government-related and other constraints, which
vary by implementation and from time to time. While a developer's
efforts might be time-consuming, such efforts would be,
nevertheless, a routine undertaking for those of ordinary skill the
art and having benefit of this disclosure.
[0057] While compositions and methods are described herein in terms
of "comprising" various components or steps, the compositions and
methods can also "consist essentially of" or "consist of" the
various components and steps.
[0058] Therefore, the present invention is well adapted to attain
the ends and advantages mentioned as well as those that are
inherent therein. The particular embodiments disclosed above are
illustrative only, as the present invention may be modified and
practiced in different but equivalent manners apparent to those
skilled in the art having the benefit of the teachings herein.
Furthermore, no limitations are intended to the details of
construction or design herein shown, other than as described in the
claims below. It is therefore evident that the particular
illustrative embodiments disclosed above may be altered, combined,
or modified and all such variations are considered within the scope
and spirit of the present invention. The invention illustratively
disclosed herein suitably may be practiced in the absence of any
element that is not specifically disclosed herein and/or any
optional element disclosed herein. While compositions and methods
are described in terms of "comprising," "containing," or
"including" various components or steps, the compositions and
methods can also "consist essentially of" or "consist of" the
various components and steps. All numbers and ranges disclosed
above may vary by some amount. Whenever a numerical range with a
lower limit and an upper limit is disclosed, any number and any
included range falling within the range is specifically disclosed.
In particular, every range of values (of the form, "from about a to
about b," or, equivalently, "from approximately a to b," or,
equivalently, "from approximately a-b") disclosed herein is to be
understood to set forth every number and range encompassed within
the broader range of values. Also, the terms in the claims have
their plain, ordinary meaning unless otherwise explicitly and
clearly defined by the patentee. Moreover, the indefinite articles
"a" or "an," as used in the claims, are defined herein to mean one
or more than one of the element that it introduces.
* * * * *